Models for measuring severity of illness and predicting hospital mortality for patients in Intensive Care Units (ICUs) have been around for quite some time. This has come about not only as a result of the desire to assess ICU performance by comparing observed and predicted mortality but also, at least in part, due to the more recent ability to capture data electronically.
Large data sets containing numerous measurements on a wide variety of patients has enabled the development of sophisticated predictive models of mortality. Without exception, these predictive models involve a one-step process. That is, information on a set of variables is collected and fed into a single logistic regression equation.
Two of the preeminent mortality prediction models for critically ill patients in the United States are the Acute Physiology and Chronic Health Evaluation (APACHE®) model and the Mortality Probability Model at Admission (MPM0). Each of these mortality prediction models utilizes multiple variables in a single logistic regression equation to predict a patient's probability of mortality. However, there are substantial differences regarding what information is used in these models.
The APACHE® prediction methodology is based on the view that the core mission of intensive care is to treat disease and maintain physiological homeostasis. The central metric is the APACHE® score. It measures severity of illness during the first day after ICU admission, using the type and extent of acute physiological abnormality (the Acute Physiology Score or APS) and physiological reserve (age and co-morbid conditions). The APS is a sum of weights incurred by 17 physiologic parameters, the weights being determined by each physiologic measure's worst value within their first day in the ICU. It reflects a patient's response to treatment within the first ICU day. These components of the APACHE® score are used in the over 70 predictive equations that make up the APACHE® System. One such equation predicts mortality before hospital discharge. This equation contains 143 variables, including terms for the APS, age, seven comorbid conditions, the time between hospital admission and ICU admission, 116 diagnostic categories, the admission source, and five additional clinical variables. In summary, the APACHE® mortality prediction model collects information based primarily on physiologic parameters collected within the first day in the ICU, and supplemented by, among other things, specific information on diagnosis.
The MPM0 mortality prediction model is a more simplistic model that utilizes information collected upon admission to the ICU or within one hour thereafter. It consists of 17 variables: 16 binary variables and the patient's age, as well as interaction terms between six of the binary variables and age. These variables were chosen to characterize a patient's acuity at the time of ICU admission, before being appreciably affected by ICU care. The MPM0 model is a much smaller model than the APACHE® mortality prediction model, is based on information collected at or within the first hour post-admission, and expresses a patient's clinical condition upon admission.
While there is a correlation between the APACHE® and MPM0 mortality model predictions (R2˜0.4), a fair proportion of the time they can give quite disparate predictions. For instance, in a sample of 4,072 admissions to twenty ICUs in a single calendar year, the two models yielded predictions that in absolute terms differed by more than 0.15 (15%) for 20% of the patients. Unequivocally, the variables and time frame for these two exemplary mortality prediction models are quite different and yet there is clearly pertinent information that is unique to each model.